UAVPNet: A Balanced and Enhanced UAV Object Detection and Pose Recognition Network

With the increasing popularity of unmanned aerial vehicles (UAVs), accurate positioning and pose recognition of UAVs by target images based on photoelectric detection become a research hotspot. To solve this issue, a multi-scale UAV-Pose dataset consisting of 1400 UAV images is contributed in this paper. In addition, a balanced and enhanced network (UAVPNet) is proposed. UAVPNet has two major features: (1) balanced feature pyramid (BFP) feature fusion structure to improve unbalanced multi-scale features; (2) VarifocalNet detection head to alleviate the foreground-background imbalance. A comparative study demonstrates that UAVPNet is superior to some state-of-the-art object detection models (such as Faster R-CNN-CARAFE, and Yolov8, etc.) in terms of detection accuracy and robustness. Specifically, UAVPNet achieves state-of-the-art 0.885 mAP on the newly-created UAV-Pose dataset, together with nearly 33.49 M parameters, 139.9G FLOPs, and 9.8 FPS. It could fully fulfill the requirements of UAV positioning and pose recognition in the intricate environment.